Implementation of AI-Based Predictive Models for Student Examination Failure Prevention in Abubakar Tafawa Balewa University, Bauchi, Bauchi State
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- NGN 5000
Background of the Study
Student academic performance is a key indicator of the success of higher education institutions. However, many students face challenges that affect their performance in examinations, leading to high failure rates. Traditional methods of identifying at-risk students rely on broad indicators, but AI-based predictive models offer a more personalized and data-driven approach to forecasting student performance and preventing examination failure. These AI models analyze student behavior, academic records, and other factors to predict the likelihood of failure and recommend tailored interventions. This study aims to implement AI-based predictive models to identify at-risk students at Abubakar Tafawa Balewa University (ATBU), Bauchi, and prevent examination failure by providing timely support and resources.
Statement of the Problem
Despite efforts to improve student performance, a significant number of students at ATBU, Bauchi, face challenges leading to examination failure. Traditional methods of identifying struggling students are not always timely or effective. AI-based predictive models can provide more accurate and proactive identification of students at risk of failure, allowing for early interventions. However, the effectiveness of these models in a university setting, specifically for preventing examination failure, has not been fully explored.
Objectives of the Study
1. To implement AI-based predictive models for student examination failure prevention at Abubakar Tafawa Balewa University, Bauchi.
2. To evaluate the accuracy of AI models in predicting student failure.
3. To assess the effectiveness of AI-driven interventions in reducing failure rates among at-risk students.
Research Questions
1. How accurate are AI-based predictive models in identifying students at risk of examination failure?
2. What are the key factors influencing student failure predictions in AI models?
3. How effective are the interventions suggested by AI models in improving student performance and reducing failure rates?
Research Hypotheses
1. AI-based predictive models will significantly improve the accuracy of identifying students at risk of examination failure compared to traditional methods.
2. AI-driven interventions will lead to a reduction in the failure rate of students at Abubakar Tafawa Balewa University, Bauchi.
3. The key factors identified by AI models (e.g., attendance, academic performance) will have a significant impact on the prediction of student failure.
Significance of the Study
This study will help Abubakar Tafawa Balewa University develop a more effective, data-driven approach to student performance prediction and intervention. The findings could lead to a more personalized approach to student support, reducing failure rates and improving overall academic success.
Scope and Limitations of the Study
The study will focus on the implementation and evaluation of AI-based predictive models at Abubakar Tafawa Balewa University. Limitations may include challenges in collecting accurate and complete data for training the AI models, as well as the initial resistance to adopting AI-based systems among faculty and students.
Definitions of Terms
• AI-Based Predictive Model: A machine learning model that uses historical data to predict future outcomes, such as the likelihood of student examination failure.
• At-Risk Students: Students identified as having a higher likelihood of failing their exams based on predictive analysis.
• Intervention: Actions taken to assist students in improving their performance, based on predictions from the AI model.
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